Improved electromyographic signal nonlinear transformation feature extraction method

The invention relates to the technical field of electromyographic signal processing, in particular to an improved electromyographic signal nonlinear transformation feature extraction method which comprises the following steps: acquiring an electromyographic signal through a sensor, and converting the electromyographic signal into a digital signal to obtain an electromyographic signal time sequence; calculating an inter-channel correlation coefficient by adopting a sliding window to obtain a correlation coefficient vector; carrying out discrete Fourier transform on the obtained correlation coefficient vector to obtain a frequency domain discrete value to represent the square of a correlation function; associating the derivative of the time domain signal with the frequency domain signal; calculating the vector even order power spectrum density of each correlation coefficient; the amplitude change of each p-order correlation coefficient is calculated; and constructing a characteristic vector based on the even-or